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Unpublished Paper
Topic Models for Taxonomies
  • Anton Bakalov
  • Andrew McCallum
  • Hanna M. Wallach, University of Massachusetts - Amherst
  • David Minmo
Concept taxonomies such as MeSH, the ACM Computing Classification System, and the NY Times Subject Headings are frequently used to help organize data. They typically consist of a set of concept names organized in a hierarchy. However, these names and structure are often not sufficient to fully capture the intended meaning of a taxonomy node, and particularly non-experts may have difficulty navigating and placing data into the taxonomy. This paper introduces two semi-supervised topic models that automatically augment a given taxonomy with many additional keywords by leveraging a corpus of multi-labeled documents. Our experiments show that users find the topics beneficial for taxonomy interpretation, substantially increasing their cataloging accuracy. Furthermore, the models provide a better information rate compared to Labeled LDA.
  • Topic modeling,
  • Taxonomy annotation,
  • Taxonomy browsing
Publication Date
This is the pre-published version harvested from CIIR.
Citation Information
Anton Bakalov, Andrew McCallum, Hanna M. Wallach and David Minmo. "Topic Models for Taxonomies" (2012)
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